Whole-Genome Prediction of Type-2 Diabetes Susceptibility in Various Populations

不同人群 2 型糖尿病易感性的全基因组预测

• 批准号: 8531237
• 负责人: Yann Charles Klimentidis
• 金额: $ 14.56万
• 依托单位: UNIVERSITY OF ARIZONA
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8531237
• 关键词: Accounting; Affect; African; Age; Architecture; Area; Biology; Biomedical Research; Body mass index; Clinical; Complex; Data; Data Analyses; Data Set; Data Sources; Developed Countries; Developing Countries; Development; Development Plans; Ethnic group; Etiology; European; Family; Family history of; Frequencies; Funding; Genes; Genetic; Genetic Markers; Genetic Risk; Genome; Genomics; Genotype; Goals; Grant; Hand; Health; Height; Human; Individual; Informatics; Longevity; Machine Learning; Malignant Neoplasms; Medicine; Mentors; Meta-Analysis; Methods; Mexican; Mexican Americans; Modeling; Non-Insulin-Dependent Diabetes Mellitus; Play; Population; Predisposition; Prevention; Prevention strategy; Publishing; Quantitative Genetics; Race; Recording of previous events; Records; Reporting; Research; Research Personnel; Risk; Risk Factors; Role; Scientist; Scoring Method; Single Nucleotide Polymorphism; Source; Statistical Methods; Testing; Training; Variant; Weight; animal breeding; base; career; career development; case control; database of Genotypes and Phenotypes; diabetes risk; disorder risk; gene environment interaction; genetic variant; genome wide association study; improved; meetings; patient oriented; predictive modeling; racial and ethnic; sex; trait

DESCRIPTION (provided by applicant): The applicant's career goal is to become a productive independent investigator in the area of statistical genetics, particularly in the area of genomic-based prediction of type-2 diabetes (T2D) risk. To meet this goal, the applicant proposes a career development plan that includes hands- on and didactic training in statistical learning as applied to quantitative genetics, categorical and case-control data analysis, informatics as applied to high dimensional genetic data, and the biology and genetics of T2D. A highly accomplished and diverse set of investigators with proven track records of successful mentoring will oversee the applicant's career development. The research component of this project seeks to improve our ability to use genetic information to predict an individual's risk of developing T2D Publically available genetic and phenotypic data from sources such as dbGaP (The database of Phenotypes and Genotypes) will be used to develop and test various models for prediction of T2D risk among three racial/ethnic groups. This project will capitalize on newly developed statistical methods that are able to incorporate information from tens of thousands of genetic markers at once, which represent a major advance over current methods that typically take fewer than 100 markers into account. The aims of the study are: 1) To test the hypothesis, in different populations, that individualized whole-genome prediction of T2D (along with standard covariates of sex, age, and BMI) will offer major improvements over current genetics-based prediction models, and will offer equal or greater accuracy than prediction based on family history; 2) To impute additional genetic markers to determine whether prediction can be improved, and to identify the subset of markers that is most useful in predicting T2D; 3) To develop prediction models for T2D risk given a certain body mass index (BMI), by including as predictors the interaction of BMI and genotypes. This project will greatly enhance our ability to predict an individual's susceptibility to T2D within various populations, leading to earlier and targeted prevention strategies, will increase our understanding of the genetic basis of T2D, and will provide critical training for Dr. Klimentidis' development as an independent scientist.

描述（由申请人提供）：申请人的职业目标是成为统计遗传学领域的一名富有成效的独立研究者，特别是在基于基因组的 2 型糖尿病 (T2D) 风险预测领域。为了实现这一目标，申请人提出了一项职业发展计划，其中包括应用于定量遗传学、分类和病例对照数据分析、应用于高维遗传数据的信息学以及 T2D 生物学和遗传学的统计学习的实践和教学培训。一群卓有成就且多元化的调查员将监督申请人的职业发展，这些调查员拥有成功指导的良好记录。该项目的研究部分旨在提高我们使用遗传信息预测个体患 T2D 风险的能力。来自 dbGaP（表型和基因型数据库）等来源的公开遗传和表型数据将用于开发和测试用于预测三个种族/民族群体中 T2D 风险的各种模型。该项目将利用新开发的统计方法，这些方法能够同时整合来自数以万计的遗传标记的信息，这代表着相对于通常考虑不到 100 个标记的当前方法的重大进步。该研究的目的是： 1) 在不同人群中检验这一假设，即 T2D 个体化全基因组预测（以及性别、年龄和 BMI 的标准协变量）将比当前基于遗传学的预测模型提供重大改进，并且将提供与基于家族史的预测相同或更高的准确性； 2) 估算额外的遗传标记以确定是否可以改进预测，并确定对预测 T2D 最有用的标记子集； 3) 通过将 BMI 和基因型的相互作用作为预测因子，开发给定体重指数 (BMI) 的 T2D 风险预测模型。该项目将大大增强我们预测不同人群中个体对 T2D 易感性的能力，从而制定更早且有针对性的预防策略，将增加我们对 T2D 遗传基础的理解，并将为 Klimentidis 博士作为独立科学家的发展提供重要的培训。